Merge branch 'main' of https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map

LICENSE

+MIT License
+
+Copyright (c) 2023 Tamino Huxohl
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

 # Mu Map
-Repository for the code to replicate the paper of Shi et al. with our SPECT/CT Scanner.
-
-## Differences 
-|                       |                        Authors |                              Ours |
-| :-------------------- | :----------------------------: | :-------------------------------: |
-| SPECT/CT Scanner      |          GE NM/CT 850 SPECT/CT | Siemens Symbia Intevo 16 SPECT/CT |
-| Scan Arc              |                           180° |                              208° |
-| Angles                |                             60 |                                34 |
-| Energy Window Lower   |                    114-126 keV |                       110-131 keV |
-| Energy Window Upper   |                    126-155 keV |                       131-152 keV |
-| Dose                  |                         15 mCi |                                 ? |
-| Reconstruction        | OSEM (5 iterations, 4 subsets) |        Siemens Conjugate Gradient |
-| CT Energy             |                        120 kVp |                                 ? |
-| Attenuation Map Shape |                  (25-35)x64x64 |                                 ? |
-| Reconstruction Shape  |                       64x64x64 |                        51x128x128 |
-| Voxel Size            |                 6.8x6.8x6.8 mm³|                   4.8x4.8x4.8 mm³ |
-
-
-## Open Questions
+Repository containing the code of the paper `Deep Learning Approximation of Attenuation Maps for Myocardial Perfusion SPECT with an IQ·SPECT Collimator`.
+
+Using this code, it is possible to train a deep neural network to predict attenuation maps from non-attenuation-corrected reconstructions.
+The training is inspired by the description in [1] and is implemented with [PyTorch](https://pytorch.org/).
+A random search procedure [2] is implemented for the optimization of hyperparameters.
+To compute attenuation-corrected reconstructions, the [script](/mu_map/scripts/compute_recons.py) implements the post-reconstruction attenuation correction (PRAC) algorithm [3].
+It uses the open-source tomographic reconstruction software [STIR](https://stir.sourceforge.net/).
+Reconstructions can be evaluated using the mean squared error or the normalized mean absolute error.
+Polar maps cannot be generated with this code, but they can be evaluated.
+For the purposes of this paper, grayscale polar maps were generated using the Cedars-Sinai Cardiac Suite [4].
+
+## Results
+
+### Comparison of Polar Maps
+![Comparison of Polar Maps](res/polar_maps.png)
+
+## Release ToDo's
+* Update dependency versions in pyproject.toml an requirements.txt
+* Test installation instructions
+* Add illustrations:
+  * Gif of the comparison between a predicted and an actual attenuation map
+  * polar maps
+
+## Installation
+Installation was tested with Ubuntu 20.04 and 22.04.
+1. Create and load a virtual environment `python -m venv .venv && source .venv/bin/activate`
+2. Install python requirements `pip install -r requirements.txt`
+
+### STIR
+STIR is required to project and reconstruct scans (using the PRAC algorithm).
+For its installation, this repository contains STIR as a submodule as well as an installation script.
+To install STIR run:
+1. `git submodule init && git submodule update`
+2. `cd libs && ./install.sh && cd ..`
+
+### Tesseract OCR
+Because we could not export perfusion scores from polar maps generated with the Cedars-Sinai Cardiac Suite, but only RGB images with scores written in a green font, we added a [script](/mu_map/polar_map/get_perfusion.py) to help extract these scores.
+This script uses [Tesseract](https://tesseract-ocr.github.io/) to automatically parse these numbers from the image.
+To install tesseract, run:
+`sudo apt install tesseract-ocr`
+
+## Usage
+This code is not intended for direct usage as data structures likely differ.
+
+## Features
+* cGAN training
+* Random search [2] for hyperparameter optimization
+* PRAC [3] algorithm
+* Evaluation of polar maps
 
 ## References
-* `Shi et al. "Deep learning-based attenuation map generation for myocardial perfusion SPECT". 2020 In: European Journal of Nuclear Medicine and Molecular Imaging 47.10`
-  * [DOI: 10.1007/s00259-020-04746-6](www.doi.org/10.1007/s00259-020-04746-6)
-
-## Install
-Install libraries for tomographic reconstruction:
-
-### NiftyRec
-* `mkdir libs`
-* `cd libs`
-* `git clone https://github.com/TomographyLab/NiftyRec`
-* `cd NiftyRec`
-* `git checkout 5329496`
-* `cp README.md README.txt` - this file is needed by cmake
-* `mkdir build`
-* `cd build`
-* `ccmake ..` - turn of the volume renderer
-* `make -j 8`
-* `make package`
-* `sudo dpkg -i NiftyRec-3.1.0-Linux-x86_64.deb`
-
-### TomoLab
-Make sure your python virtual environment is active for this.
-* `mkdir libs`
-* `cd libs`
-* `git clone https://github.com/TomographyLab/TomoLab`
-* `git checkout 86b9a58`
+1. L. Shi et al. “Deep learning-based attenuation map generation for myocardial perfusion SPECT”. In: European Journal of Nuclear Medicine and Molecular Imaging 47 (2020). doi: [10.1007/s00259-020-04746-6](https://doi.org/10.1007/s00259-020-04746-6).
+2. J. Bergstra and Y. Bengio. “Random Search for Hyper-Parameter Optimization”. In: Journal of Machine Learning Research 13 (2012).
+3. H. Liu et al. “Post-reconstruction attenuation correction for SPECT myocardium perfusion imaging facilitated by deep learning-based attenuation map generation”. In: Journal of Nuclear Cardiology 29 (2021). doi: [10.1007/s12350-021-02817-1](http://doi.org/10.1007/s12350-021-02817-1).
+4. G. Germano et al. “Quantitation in gated perfusion SPECT imaging: The Cedars-Sinai approach”. In: Journal of Nuclear Cardiology 14 (2007). doi: [10.1016/j.nuclcard.2007.06.008](https://doi.org/10.1016/j.nuclcard.2007.06.008).
+

mu_map/scripts/create_video.py

+import os
+
+import cv2 as cv
+import numpy as np
+import torch
+
+from mu_map.dataset.default import MuMapDataset
+from mu_map.dataset.transform import PadCropTranform, SequenceTransform
+from mu_map.models.unet import UNet
+from mu_map.util import to_grayscale, COLOR_WHITE
+from mu_map.random_search.cgan import load_params
+from mu_map.vis.slices import join_images
+
+torch.set_grad_enabled(False)
+
+random_search_iter_dir = "cgan_random_search/001"
+
+params = load_params(os.path.join(random_search_iter_dir, "params.json"))
+print(params["normalization"])
+print(params["generator_features"])
+dataset = MuMapDataset(
+    "data/second/",
+    transform_normalization=SequenceTransform(
+        [params["normalization"], PadCropTranform(dim=3, size=32)]
+    ),
+    split_name="test",
+    scatter_correction=False,
+)
+recon, mu_map_ct = dataset[1]
+mu_map_ct = mu_map_ct.squeeze().numpy()
+
+device = torch.device("cpu")
+model = UNet(features=params["generator_features"])
+model.load_state_dict(
+    torch.load(
+        os.path.join(random_search_iter_dir, "snapshots/val_min_generator.pth"), map_location=device
+    )
+)
+
+mu_map_dl = model(recon.unsqueeze(dim=0)).squeeze().numpy()
+mu_map_dl = np.clip(mu_map_dl, 0, mu_map_ct.max())
+
+volumes = [mu_map_ct, mu_map_dl, np.abs(mu_map_dl - mu_map_ct)]
+min_val = 0
+max_val = max(mu_map_ct.max(), mu_map_dl.max())
+print(mu_map_ct.max(), mu_map_dl.max())
+
+fourcc = cv.VideoWriter_fourcc(*"mp4v")
+frame_size = (512, 3 * 512 + 2 * 10)
+print(f"Frame size {frame_size}")
+# video_writer = cv.VideoWriter("mu_map_comparison.mp4", fourcc, 25, frame_size, isColor=False)
+video_writer = cv.VideoWriter("mu_map_comparison.mp4", cv.VideoWriter_fourcc(*"mp4v"), 25, frame_size[::-1], isColor=False)
+for i in range(mu_map_ct.shape[0]):
+    images = map(lambda volume: volume[i], volumes)
+    images = map(
+        lambda img: to_grayscale(img, min_val=min_val, max_val=max_val), images
+    )
+    images = map(lambda img: cv.resize(img, (512, 512)), images)
+    images = list(images)
+
+    txt = f"{str(i):{len(str(mu_map_ct.shape[0]))}}/{mu_map_ct.shape[0]}"
+    cv.putText(images[0], txt, (0, 30), cv.FONT_HERSHEY_SIMPLEX, 1, 255, 3)
+
+    image = join_images(images)
+    for i in range(5):
+        video_writer.write(image)
+video_writer.release()

pyproject.toml

+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "mu_map"
+version = "0.0.1"
+authors = [
+  { name="Tamino Huxohl", email="thuxohl@hdz-nrw.de"  },
+  
+]
+description = "Deep learning approximation of Attenuation Maps from SPECT reconstructions"
+readme = "README.md"
+requires-python = ">=3.8"
+classifiers = [
+    "Environment :: GPU :: NVIDIA CUDA",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: Unix",
+    "Programming Language :: Python :: 3",
+    "Topic :: Scientific/Engineering :: Artificial Life",
+]
+dependencies = [
+    "matplotlib>=3.5.3",
+    "numpy>=1.23.1",
+    "opencv-python>=4.6.0.66",
+    "pandas>=1.4.3",
+    "pydicom>=2.3.0",
+    "pytesseract",
+    "scipy",
+    "stir>=5.0.0",
+    "termcolor",
+    "torch>=1.12.1",
+    "torchvision>=0.13.1",
+]
+
+[project.urls]
+"Homepage" = "https://gitlab.ub.uni-bielefeld.de/thuxohl/mu-map"